Image Synthesis From Text With Deep Learning

• Paper: StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networks

Abstract

Synthesizing photo-realistic images from text descriptions is a challenging problem in computer vision and has many practical applications. Samples generated by existing text-to-image approaches can roughly reflect the meaning of the given descriptions, but they fail to contain necessary details and vivid object parts. In this paper, we propose stacked Generative Adversarial Networks (StackGAN) to generate photo-realistic images conditioned on text descriptions. The Stage-I GAN sketches the primitive shape and basic colors of the object based on the given text description, yielding Stage-I low resolution images. The Stage-II GAN takes Stage-I results and text descriptions as inputs, and generates high resolution images with photo-realistic details. The Stage-II GAN is able to rectify defects and add compelling details with the refinement process. Samples generated by StackGAN are more plausible than those generated by existing approaches. Importantly, our StackGAN for the first time generates realistic 256 x 256 images conditioned on only text descriptions, while state-of-the-art methods can generate at most 128 x 128 images. To demonstrate the effectiveness of the proposed StackGAN, extensive experiments are conducted on CUB and Oxford-102 datasets, which contain enough object appearance variations and are widely-used for text-to-image generation analysis.